Waypoint-based navigation.
Prior work on waypoint-based navigation repeatedly predicts intermediate goals en route to the target, then uses low-level navigators to reach these intermediate goals
 [1]}, [2]}, [3]}, [4]}, [5]}. Such policies can be learned through reinforcement learning [2]}, [1]}, [8]}, [9]}, [10]}, supervised learning [11]}, [4]}, [3]}, or just analytical planning without any learning [14]}. Potential functions can be interpreted as a Q-value function for ObjectNav [10]}, but predicted only on frontiers and learned in a supervised fashion from collections of top-down semantic maps. The approach in [11]} learns to predict value functions at the frontiers for PointGoal navigation, i.e., to a known \((x, y)\)  location, in synthetically generated mazes and lab floor-plans. In contrast, we tackle the more challenging ObjectNav task where the goal location is not known a priori, and we focus on diverse real-world indoor environments where semantic reasoning is required to find the goal (e.g., object-object and object-room relationships). We introduce the area potential function to encourage exploration and information gathering, and the object potential function to perform semantic reasoning. We empirically demonstrate the value of these components for ObjectNav in sec:results.
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